{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset URL: https://www.kaggle.com/datasets/usamabuttar/significant-earthquakes\n",
      "数据集已下载并解压！\n"
     ]
    }
   ],
   "source": [
    "from kaggle.api.kaggle_api_extended import KaggleApi\n",
    "\n",
    "# 创建 Kaggle API 实例\n",
    "api = KaggleApi()\n",
    "api.authenticate()  # 进行身份验证\n",
    "\n",
    "# 数据集名称，可以在 Kaggle 数据集页面找到\n",
    "dataset_slug = \"usamabuttar/significant-earthquakes\"  # 这是你提到的数据集\n",
    "download_path = './'  # 设置下载文件夹路径（例如当前文件夹）\n",
    "\n",
    "# 下载数据集并解压\n",
    "api.dataset_download_files(dataset_slug, path=download_path, unzip=True)\n",
    "\n",
    "print(\"数据集已下载并解压！\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据集基本信息：\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 104477 entries, 0 to 104476\n",
      "Data columns (total 23 columns):\n",
      " #   Column           Non-Null Count   Dtype  \n",
      "---  ------           --------------   -----  \n",
      " 0   Unnamed: 0       104477 non-null  int64  \n",
      " 1   time             104477 non-null  object \n",
      " 2   latitude         104477 non-null  float64\n",
      " 3   longitude        104477 non-null  float64\n",
      " 4   depth            104192 non-null  float64\n",
      " 5   mag              104477 non-null  float64\n",
      " 6   magType          104477 non-null  object \n",
      " 7   nst              33877 non-null   float64\n",
      " 8   gap              44170 non-null   float64\n",
      " 9   dmin             24226 non-null   float64\n",
      " 10  rms              75732 non-null   float64\n",
      " 11  net              104477 non-null  object \n",
      " 12  id               104477 non-null  object \n",
      " 13  updated          104477 non-null  object \n",
      " 14  place            103586 non-null  object \n",
      " 15  type             104477 non-null  object \n",
      " 16  horizontalError  22850 non-null   float64\n",
      " 17  depthError       54764 non-null   float64\n",
      " 18  magError         37473 non-null   float64\n",
      " 19  magNst           44496 non-null   float64\n",
      " 20  status           104477 non-null  object \n",
      " 21  locationSource   104477 non-null  object \n",
      " 22  magSource        104477 non-null  object \n",
      "dtypes: float64(12), int64(1), object(10)\n",
      "memory usage: 18.3+ MB\n",
      "None\n",
      "\n",
      "数据集前五行：\n",
      "   Unnamed: 0                      time  latitude  longitude  depth   mag  \\\n",
      "0           0  1900-10-09T12:25:00.000Z     57.09    -153.48    NaN  7.86   \n",
      "1           1  1901-03-03T07:45:00.000Z     36.00    -120.50    NaN  6.40   \n",
      "2           2  1901-07-26T22:20:00.000Z     40.80    -115.70    NaN  5.00   \n",
      "3           3  1901-12-30T22:34:00.000Z     52.00    -160.00    NaN  7.00   \n",
      "4           4  1902-01-01T05:20:30.000Z     52.38    -167.45    NaN  7.00   \n",
      "\n",
      "  magType  nst  gap  dmin  ...                   updated  \\\n",
      "0      mw  NaN  NaN   NaN  ...  2022-05-09T14:44:17.838Z   \n",
      "1      ms  NaN  NaN   NaN  ...  2018-06-04T20:43:44.000Z   \n",
      "2      fa  NaN  NaN   NaN  ...  2018-06-04T20:43:44.000Z   \n",
      "3      ms  NaN  NaN   NaN  ...  2018-06-04T20:43:44.000Z   \n",
      "4      ms  NaN  NaN   NaN  ...  2018-06-04T20:43:44.000Z   \n",
      "\n",
      "                                place        type horizontalError depthError  \\\n",
      "0      16 km SW of Old Harbor, Alaska  earthquake             NaN        NaN   \n",
      "1  12 km NNW of Parkfield, California  earthquake             NaN        NaN   \n",
      "2             6 km SE of Elko, Nevada  earthquake             NaN        NaN   \n",
      "3                     south of Alaska  earthquake             NaN        NaN   \n",
      "4      113 km ESE of Nikolski, Alaska  earthquake             NaN        NaN   \n",
      "\n",
      "  magError  magNst    status  locationSource  magSource  \n",
      "0      NaN     NaN  reviewed           ushis         pt  \n",
      "1      NaN     NaN  reviewed           ushis        ell  \n",
      "2      NaN     NaN  reviewed           ushis        sjg  \n",
      "3      NaN     NaN  reviewed           ushis        abe  \n",
      "4      NaN     NaN  reviewed           ushis        abe  \n",
      "\n",
      "[5 rows x 23 columns]\n",
      "\n",
      "数据统计信息：\n",
      "          Unnamed: 0       latitude      longitude          depth  \\\n",
      "count  104477.000000  104477.000000  104477.000000  104192.000000   \n",
      "mean    52238.000000       3.286102      40.517705      62.303356   \n",
      "std     30160.056374      30.055200     121.958801     108.704034   \n",
      "min         0.000000     -77.080000    -179.997000      -4.000000   \n",
      "25%     26119.000000     -17.910000     -72.240000      11.100000   \n",
      "50%     52238.000000      -1.164200      99.636000      33.000000   \n",
      "75%     78357.000000      28.919000     142.672000      51.500000   \n",
      "max    104476.000000      87.386000     180.000000     700.000000   \n",
      "\n",
      "                 mag           nst           gap          dmin           rms  \\\n",
      "count  104477.000000  33877.000000  44170.000000  24226.000000  75732.000000   \n",
      "mean        5.447417    148.773091     63.578516      4.297252      0.955813   \n",
      "std         0.481816    122.950435     38.423362      5.296356      0.371313   \n",
      "min         5.000000      0.000000      6.500000      0.000000     -1.000000   \n",
      "25%         5.100000     64.000000     36.000000      1.289250      0.810000   \n",
      "50%         5.300000    109.000000     55.100000      2.597000      0.960000   \n",
      "75%         5.680000    195.000000     81.000000      5.126750      1.100000   \n",
      "max         9.500000    929.000000    360.000000     50.901000     69.320000   \n",
      "\n",
      "       horizontalError    depthError      magError        magNst  \n",
      "count     22850.000000  54764.000000  37473.000000  44496.000000  \n",
      "mean          7.746670      7.701234      0.161871     56.346615  \n",
      "std           4.185453     10.529593      0.151378     83.126465  \n",
      "min           0.000000     -1.000000      0.000000      0.000000  \n",
      "25%           6.100000      1.900000      0.058000     13.000000  \n",
      "50%           7.600000      4.800000      0.089000     28.000000  \n",
      "75%           9.250000      9.000000      0.200000     63.000000  \n",
      "max          99.000000   1091.900000      1.840000    954.000000  \n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# 加载地震数据集\n",
    "file_path = 'Significant_Earthquakes.csv'\n",
    "data = pd.read_csv(file_path)\n",
    "\n",
    "# 查看数据基本信息\n",
    "print(\"数据集基本信息：\")\n",
    "print(data.info())\n",
    "\n",
    "# 查看前几行数据\n",
    "print(\"\\n数据集前五行：\")\n",
    "print(data.head())\n",
    "\n",
    "# 查看数据统计信息\n",
    "print(\"\\n数据统计信息：\")\n",
    "print(data.describe())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_path = 'Significant_Earthquakes.csv'\n",
    "data = pd.read_csv(file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "missing_values = data.isnull().sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\GARLANG\\AppData\\Local\\Temp\\ipykernel_11892\\3380546596.py:2: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(x=missing_values.values, y=missing_values.index, palette=\"viridis\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x=missing_values.values, y=missing_values.index, palette=\"viridis\")\n",
    "plt.title(\"Missing Values by Column\")\n",
    "plt.xlabel(\"Number of Missing Values\")\n",
    "plt.ylabel(\"Columns\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(data['mag'], bins=30, kde=True, color=\"blue\")\n",
    "plt.title(\"Distribution of Earthquake Magnitudes (mag)\")\n",
    "plt.xlabel(\"Magnitude (mag)\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "numerical_columns = data.select_dtypes(include=['float64']).columns\n",
    "correlation_matrix = data[numerical_columns].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 8))\n",
    "sns.heatmap(correlation_matrix, annot=True, cmap=\"coolwarm\", fmt=\".2f\", linewidths=0.5)\n",
    "plt.title(\"Correlation Matrix of Numerical Features\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing values per column after cleaning:\n",
      "Unnamed: 0            0\n",
      "time                  0\n",
      "latitude              0\n",
      "longitude             0\n",
      "depth                 0\n",
      "mag                   0\n",
      "magType               0\n",
      "nst                   0\n",
      "gap                   0\n",
      "net                   0\n",
      "id                    0\n",
      "updated               0\n",
      "place               891\n",
      "type                  0\n",
      "magError              0\n",
      "magNst            59981\n",
      "status                0\n",
      "locationSource        0\n",
      "magSource             0\n",
      "depth_nst             0\n",
      "lat_lon               0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "import numpy as np\n",
    "# Step 1: 删除相关性低且缺失值较多的特征\n",
    "columns_to_drop = ['horizontalError', 'dmin', 'rms', 'depthError']\n",
    "existing_columns_to_drop = [col for col in columns_to_drop if col in data.columns]\n",
    "data_cleaned = data.drop(columns=existing_columns_to_drop)\n",
    "\n",
    "# Step 2: 填充缺失值\n",
    "# 填充 'depth' 缺失值（中位数填充）\n",
    "if 'depth' in data_cleaned.columns:\n",
    "    data_cleaned['depth'] = data_cleaned['depth'].fillna(data_cleaned['depth'].median())\n",
    "\n",
    "# 填充 'gap', 'nst', 'magError' 缺失值（均值填充）\n",
    "for col in ['gap', 'nst', 'magError']:\n",
    "    if col in data_cleaned.columns:\n",
    "        data_cleaned[col] = data_cleaned[col].fillna(data_cleaned[col].mean())\n",
    "\n",
    "# Step 3: 生成交互特征\n",
    "if 'depth' in data_cleaned.columns and 'nst' in data_cleaned.columns:\n",
    "    data_cleaned['depth_nst'] = data_cleaned['depth'] * data_cleaned['nst']\n",
    "if 'latitude' in data_cleaned.columns and 'longitude' in data_cleaned.columns:\n",
    "    data_cleaned['lat_lon'] = data_cleaned['latitude'] * data_cleaned['longitude']\n",
    "\n",
    "# Step 4: 检查是否有缺失值\n",
    "missing_values = data_cleaned.isnull().sum()\n",
    "print(\"Missing values per column after cleaning:\")\n",
    "print(missing_values)\n",
    "\n",
    "# Step 5: 对目标变量 'mag' 进行对数变换\n",
    "if 'mag' in data_cleaned.columns:\n",
    "    data_cleaned['log_mag'] = data_cleaned['mag'].apply(lambda x: np.log(x) if x > 0 else x)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing values in selected features before filling:\n",
      "depth        0\n",
      "nst          0\n",
      "gap          0\n",
      "latitude     0\n",
      "longitude    0\n",
      "depth_nst    0\n",
      "lat_lon      0\n",
      "magError     0\n",
      "dtype: int64\n",
      "Shapes of the datasets:\n",
      "X_train: (83581, 8)\n",
      "X_test: (20896, 8)\n",
      "y_train: (83581,)\n",
      "y_test: (20896,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Step 1: Finalize feature selection\n",
    "final_selected_features = ['depth', 'nst', 'gap', 'latitude', 'longitude', 'depth_nst', 'lat_lon', 'magError']\n",
    "\n",
    "# Ensure selected features exist in the cleaned dataset\n",
    "final_selected_features = [feature for feature in final_selected_features if feature in data_cleaned.columns]\n",
    "\n",
    "# Define the target variable\n",
    "target_variable = 'log_mag'\n",
    "\n",
    "# Step 2: Prepare features (X) and target (y)\n",
    "X = data_cleaned[final_selected_features]\n",
    "y = data_cleaned[target_variable]\n",
    "\n",
    "# Step 3: Check for any remaining missing values\n",
    "missing_values_in_X = X.isnull().sum()\n",
    "print(\"Missing values in selected features before filling:\")\n",
    "print(missing_values_in_X)\n",
    "\n",
    "# Fill any remaining missing values in X with mean (if any)\n",
    "X = X.fillna(X.mean())\n",
    "\n",
    "# Step 4: Split the dataset into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Output the shapes of the training and testing sets\n",
    "print(\"Shapes of the datasets:\")\n",
    "print(f\"X_train: {X_train.shape}\")\n",
    "print(f\"X_test: {X_test.shape}\")\n",
    "print(f\"y_train: {y_train.shape}\")\n",
    "print(f\"y_test: {y_test.shape}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 0.006237726844284478\n",
      "RMSE: 0.07897928110767075\n",
      "R²: 0.14439270088663791\n",
      "\n",
      "Feature Coefficients:\n",
      "     Feature   Coefficient\n",
      "7   magError  2.924725e-01\n",
      "1        nst  2.461336e-04\n",
      "5  depth_nst  1.143975e-08\n",
      "6    lat_lon -7.942775e-07\n",
      "0      depth -6.982155e-06\n",
      "4  longitude -1.831848e-05\n",
      "3   latitude -3.528467e-05\n",
      "2        gap -3.680215e-04\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "# Step 1: Sample 10% of the full dataset for training and testing\n",
    "X_sampled = X.sample(frac=0.1, random_state=42)\n",
    "y_sampled = y.loc[X_sampled.index]\n",
    "\n",
    "# Step 2: Split the sampled data into training and testing sets\n",
    "X_train_lr, X_test_lr, y_train_lr, y_test_lr = train_test_split(X_sampled, y_sampled, test_size=0.2, random_state=42)\n",
    "\n",
    "# Step 3: Initialize the Linear Regression model\n",
    "lr_model = LinearRegression()\n",
    "\n",
    "# Step 4: Train the model on the sampled training data\n",
    "lr_model.fit(X_train_lr, y_train_lr)\n",
    "\n",
    "# Step 5: Predict on the sampled test data\n",
    "y_pred_lr = lr_model.predict(X_test_lr)\n",
    "\n",
    "# Step 6: Evaluate the model performance\n",
    "mse_lr = mean_squared_error(y_test_lr, y_pred_lr)\n",
    "rmse_lr = np.sqrt(mse_lr)\n",
    "r2_lr = r2_score(y_test_lr, y_pred_lr)\n",
    "\n",
    "# Step 7: Analyze coefficients\n",
    "coefficients_lr = pd.DataFrame({\n",
    "    'Feature': X_train_lr.columns,\n",
    "    'Coefficient': lr_model.coef_\n",
    "}).sort_values(by='Coefficient', ascending=False)\n",
    "\n",
    "# Output results\n",
    "print(f\"MSE: {mse_lr}\")\n",
    "print(f\"RMSE: {rmse_lr}\")\n",
    "print(f\"R²: {r2_lr}\")\n",
    "\n",
    "print(\"\\nFeature Coefficients:\")\n",
    "print(coefficients_lr)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Performance:\n",
      "MSE: 0.00475417311018436\n",
      "RMSE: 0.06895051203714415\n",
      "R²: 0.32371016393209073\n",
      "\n",
      "Feature Importances:\n",
      "     Feature  Importance\n",
      "7   magError    0.229778\n",
      "4  longitude    0.167059\n",
      "3   latitude    0.155483\n",
      "6    lat_lon    0.125487\n",
      "2        gap    0.091864\n",
      "1        nst    0.085495\n",
      "5  depth_nst    0.074951\n",
      "0      depth    0.069883\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "import pandas as pd\n",
    "\n",
    "# Step 1: Sample 10% of the training data to reduce memory usage\n",
    "X_train_sampled = X_train.sample(frac=0.1, random_state=42)\n",
    "y_train_sampled = y_train.loc[X_train_sampled.index]\n",
    "\n",
    "# Step 2: Initialize the Random Forest model\n",
    "rf_model = RandomForestRegressor(n_estimators=100, random_state=42)\n",
    "\n",
    "# Step 3: Train the model on the sampled training data\n",
    "rf_model.fit(X_train_sampled, y_train_sampled)\n",
    "\n",
    "# Step 4: Predict on the test data\n",
    "y_pred = rf_model.predict(X_test)\n",
    "\n",
    "# Step 5: Evaluate the model performance\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "rmse = np.sqrt(mse)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "# Step 6: Analyze feature importance\n",
    "feature_importances = rf_model.feature_importances_\n",
    "feature_importance_df = pd.DataFrame({\n",
    "    'Feature': X_train.columns,\n",
    "    'Importance': feature_importances\n",
    "}).sort_values(by='Importance', ascending=False)\n",
    "\n",
    "# Output results\n",
    "print(\"Model Performance:\")\n",
    "print(f\"MSE: {mse}\")\n",
    "print(f\"RMSE: {rmse}\")\n",
    "print(f\"R²: {r2}\")\n",
    "\n",
    "print(\"\\nFeature Importances:\")\n",
    "print(feature_importance_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gradient Boosting Regressor Performance (Before Feature Removal):\n",
      "MSE: 0.004258294734112037\n",
      "RMSE: 0.06525561074813442\n",
      "R²: 0.39424977153392005\n",
      "\n",
      "Feature Importances:\n",
      "     Feature  Importance\n",
      "7   magError    0.559039\n",
      "2        gap    0.159051\n",
      "1        nst    0.104736\n",
      "3   latitude    0.042698\n",
      "4  longitude    0.041370\n",
      "0      depth    0.041131\n",
      "5  depth_nst    0.027669\n",
      "6    lat_lon    0.024306\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "import pandas as pd\n",
    "\n",
    "# Step 1: Use the original feature set (before removing low-importance features)\n",
    "original_selected_features = ['depth', 'nst', 'gap', 'latitude', 'longitude', 'depth_nst', 'lat_lon', 'magError']\n",
    "\n",
    "# Prepare the original feature set\n",
    "X_original = X[original_selected_features]\n",
    "y_original = y\n",
    "\n",
    "# Reset index to ensure alignment\n",
    "X_original = X_original.reset_index(drop=True)\n",
    "y_original = y_original.reset_index(drop=True)\n",
    "\n",
    "# Step 2: Sample 10% of the training data to reduce memory usage\n",
    "X_train_sampled_original = X_original.sample(frac=0.1, random_state=42)\n",
    "y_train_sampled_original = y_original.loc[X_train_sampled_original.index]\n",
    "\n",
    "# Step 3: Initialize the Gradient Boosting Regressor\n",
    "gbr_model_original = GradientBoostingRegressor(n_estimators=100, learning_rate=0.1, max_depth=3, random_state=42)\n",
    "\n",
    "# Step 4: Train the model on the sampled training data\n",
    "gbr_model_original.fit(X_train_sampled_original, y_train_sampled_original)\n",
    "\n",
    "# Step 5: Predict on the test data\n",
    "y_pred_gbr_original = gbr_model_original.predict(X_test[original_selected_features])\n",
    "\n",
    "# Step 6: Evaluate the model performance\n",
    "mse_gbr_original = mean_squared_error(y_test, y_pred_gbr_original)\n",
    "rmse_gbr_original = np.sqrt(mse_gbr_original)\n",
    "r2_gbr_original = r2_score(y_test, y_pred_gbr_original)\n",
    "\n",
    "# Step 7: Analyze feature importance\n",
    "feature_importances_gbr_original = gbr_model_original.feature_importances_\n",
    "feature_importance_df_gbr_original = pd.DataFrame({\n",
    "    'Feature': original_selected_features,\n",
    "    'Importance': feature_importances_gbr_original\n",
    "}).sort_values(by='Importance', ascending=False)\n",
    "\n",
    "# Output results\n",
    "print(\"Gradient Boosting Regressor Performance (Before Feature Removal):\")\n",
    "print(f\"MSE: {mse_gbr_original}\")\n",
    "print(f\"RMSE: {rmse_gbr_original}\")\n",
    "print(f\"R²: {r2_gbr_original}\")\n",
    "\n",
    "print(\"\\nFeature Importances:\")\n",
    "print(feature_importance_df_gbr_original)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost Regressor Performance:\n",
      "MSE: 0.004253288509538818\n",
      "RMSE: 0.06521724089179808\n",
      "R²: 0.39496191615244225\n",
      "\n",
      "Feature Importances:\n",
      "     Feature  Importance\n",
      "7   magError    0.423691\n",
      "2        gap    0.160782\n",
      "1        nst    0.149496\n",
      "5  depth_nst    0.069784\n",
      "4  longitude    0.061581\n",
      "0      depth    0.045654\n",
      "3   latitude    0.045561\n",
      "6    lat_lon    0.043452\n"
     ]
    }
   ],
   "source": [
    "from xgboost import XGBRegressor\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "import pandas as pd\n",
    "\n",
    "# Step 1: Use the original feature set (before removing low-importance features)\n",
    "original_selected_features = ['depth', 'nst', 'gap', 'latitude', 'longitude', 'depth_nst', 'lat_lon', 'magError']\n",
    "\n",
    "# Prepare the original feature set\n",
    "\n",
    "X_original = X[original_selected_features]\n",
    "y_original = y\n",
    "\n",
    "# Reset index to ensure alignment\n",
    "X_original = X_original.reset_index(drop=True)\n",
    "y_original = y_original.reset_index(drop=True)\n",
    "\n",
    "# Step 2: Sample 10% of the training data to reduce memory usage\n",
    "X_train_sampled_original = X_original.sample(frac=0.1, random_state=42)\n",
    "y_train_sampled_original = y_original.loc[X_train_sampled_original.index]\n",
    "\n",
    "# Step 3: Initialize the XGBoost Regressor\n",
    "xgb_model = XGBRegressor(n_estimators=100, learning_rate=0.1, max_depth=3, random_state=42)\n",
    "\n",
    "# Step 4: Train the model on the sampled training data\n",
    "xgb_model.fit(X_train_sampled_original, y_train_sampled_original)\n",
    "\n",
    "# Step 5: Predict on the test data\n",
    "y_pred_xgb = xgb_model.predict(X_test[original_selected_features])\n",
    "\n",
    "# Step 6: Evaluate the model performance\n",
    "mse_xgb = mean_squared_error(y_test, y_pred_xgb)\n",
    "rmse_xgb = np.sqrt(mse_xgb)\n",
    "r2_xgb = r2_score(y_test, y_pred_xgb)\n",
    "\n",
    "# Step 7: Analyze feature importance\n",
    "feature_importances_xgb = xgb_model.feature_importances_\n",
    "feature_importance_df_xgb = pd.DataFrame({\n",
    "    'Feature': original_selected_features,\n",
    "    'Importance': feature_importances_xgb\n",
    "}).sort_values(by='Importance', ascending=False)\n",
    "\n",
    "# Output results\n",
    "print(\"XGBoost Regressor Performance:\")\n",
    "print(f\"MSE: {mse_xgb}\")\n",
    "print(f\"RMSE: {rmse_xgb}\")\n",
    "print(f\"R²: {r2_xgb}\")\n",
    "\n",
    "print(\"\\nFeature Importances:\")\n",
    "print(feature_importance_df_xgb)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost Regressor Performance:\n",
      "MSE: 0.003765302494269098\n",
      "RMSE: 0.06136206070748519\n",
      "R²: 0.46437882096880756\n",
      "\n",
      "Feature Importances:\n",
      "     Feature  Importance\n",
      "6   magError    0.434361\n",
      "2        gap    0.171231\n",
      "1        nst    0.153827\n",
      "4  longitude    0.069548\n",
      "3   latitude    0.061953\n",
      "0      depth    0.059940\n",
      "5  depth_nst    0.049140\n"
     ]
    }
   ],
   "source": [
    "from xgboost import XGBRegressor\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "import pandas as pd\n",
    "\n",
    "# Step 1: Use the original feature set (before removing low-importance features)\n",
    "original_selected_features = ['depth', 'nst', 'gap', 'latitude', 'longitude', 'depth_nst' , 'magError']\n",
    "\n",
    "# Prepare the original feature set\n",
    "\n",
    "X_original = X[original_selected_features]\n",
    "y_original = y\n",
    "\n",
    "# Reset index to ensure alignment\n",
    "X_original = X_original.reset_index(drop=True)\n",
    "y_original = y_original.reset_index(drop=True)\n",
    "\n",
    "# Step 2: Sample 10% of the training data to reduce memory usage\n",
    "X_train_sampled_original = X_original.sample(frac=0.1, random_state=42)\n",
    "y_train_sampled_original = y_original.loc[X_train_sampled_original.index]\n",
    "\n",
    "# Step 3: Initialize the XGBoost Regressor\n",
    "xgb_model = XGBRegressor(n_estimators=300, learning_rate=0.05, max_depth=5, random_state=42)\n",
    "\n",
    "# Step 4: Train the model on the sampled training data\n",
    "xgb_model.fit(X_train_sampled_original, y_train_sampled_original)\n",
    "\n",
    "# Step 5: Predict on the test data\n",
    "y_pred_xgb = xgb_model.predict(X_test[original_selected_features])\n",
    "\n",
    "# Step 6: Evaluate the model performance\n",
    "mse_xgb = mean_squared_error(y_test, y_pred_xgb)\n",
    "rmse_xgb = np.sqrt(mse_xgb)\n",
    "r2_xgb = r2_score(y_test, y_pred_xgb)\n",
    "\n",
    "# Step 7: Analyze feature importance\n",
    "feature_importances_xgb = xgb_model.feature_importances_\n",
    "feature_importance_df_xgb = pd.DataFrame({\n",
    "    'Feature': original_selected_features,\n",
    "    'Importance': feature_importances_xgb\n",
    "}).sort_values(by='Importance', ascending=False)\n",
    "\n",
    "# Output results\n",
    "print(\"XGBoost Regressor Performance:\")\n",
    "print(f\"MSE: {mse_xgb}\")\n",
    "print(f\"RMSE: {rmse_xgb}\")\n",
    "print(f\"R²: {r2_xgb}\")\n",
    "\n",
    "print(\"\\nFeature Importances:\")\n",
    "print(feature_importance_df_xgb)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All expected columns are present.\n",
      "Optimized training data shape: (83581, 8)\n",
      "Optimized testing data shape: (20896, 8)\n"
     ]
    }
   ],
   "source": [
    "# Step 1: Validate the dataset structure and column names\n",
    "# Expected columns for optimization\n",
    "expected_columns = ['depth', 'nst', 'gap', 'latitude', 'longitude', 'magError']\n",
    "\n",
    "# Check if the expected columns exist\n",
    "missing_columns = [col for col in expected_columns if col not in X.columns]\n",
    "if missing_columns:\n",
    "    print(f\"Missing columns: {missing_columns}\")\n",
    "else:\n",
    "    print(\"All expected columns are present.\")\n",
    "\n",
    "# Step 2: Create a copy of the relevant features\n",
    "X_optimized = X[expected_columns].copy()\n",
    "\n",
    "# Step 3: Generate interaction features\n",
    "# Create 'gap_nst' and 'depth_magError' as interaction features\n",
    "X_optimized['gap_nst'] = X_optimized['gap'] * X_optimized['nst']\n",
    "X_optimized['depth_magError'] = X_optimized['depth'] / (X_optimized['magError'] + 1e-6)  # Avoid division by zero\n",
    "\n",
    "# Step 4: Split the dataset\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train_opt, X_test_opt, y_train_opt, y_test_opt = train_test_split(\n",
    "    X_optimized, y, test_size=0.2, random_state=42\n",
    ")\n",
    "\n",
    "# Output dataset shapes\n",
    "print(f\"Optimized training data shape: {X_train_opt.shape}\")\n",
    "print(f\"Optimized testing data shape: {X_test_opt.shape}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Proceeding with optimized dataset...\n",
      "Training set shape: (83581, 8)\n",
      "Testing set shape: (20896, 8)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Step 1: Verify the optimized dataset\n",
    "if 'X_optimized' in locals():\n",
    "    print(\"Proceeding with optimized dataset...\")\n",
    "else:\n",
    "    print(\"Error: X_optimized is not defined. Ensure earlier steps were executed successfully.\")\n",
    "\n",
    "# Step 2: Split the dataset into training and testing sets\n",
    "try:\n",
    "    X_train_opt, X_test_opt, y_train_opt, y_test_opt = train_test_split(\n",
    "        X_optimized, y, test_size=0.2, random_state=42\n",
    "    )\n",
    "\n",
    "    # Output dataset shapes\n",
    "    print(f\"Training set shape: {X_train_opt.shape}\")\n",
    "    print(f\"Testing set shape: {X_test_opt.shape}\")\n",
    "\n",
    "except Exception as e:\n",
    "    print(f\"Error during train-test split: {e}\")\n"
   ]
  }
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